Lifetime Prediction for Organic Coatings via Feature Integration of Multi-Scale Images

doi:10.11902/1005.4537.2025.092

Journal of Chinese Society for Corrosion and protection

2025, Vol. 45

Issue (5): 1205-1218 DOI: 10.11902/1005.4537.2025.092

Current Issue | Archive | Adv Search

Lifetime Prediction for Organic Coatings via Feature Integration of Multi-Scale Images

LI Jie¹, MENG Fandi¹(

), SUN Xuesi¹, LI Jiani², CHEN Sihan², LI Zelan², CHI Jianning², QI Haixia³(

), WANG Fuhui¹, LIU Li¹

1 Center for Corrosion and Protection, Northeastern University, Shenyang 110819, China
2 Faculty of Robot Science and Engineering, Northeastern University, Shenyang 110819, China
3 National Key Laboratory of Marine Corrosion and Protection, 725th Research Institute of China State Shipbuilding Corporation, Xiamen 361100, China

Cite this article:

LI Jie, MENG Fandi, SUN Xuesi, LI Jiani, CHEN Sihan, LI Zelan, CHI Jianning, QI Haixia, WANG Fuhui, LIU Li. Lifetime Prediction for Organic Coatings via Feature Integration of Multi-Scale Images. Journal of Chinese Society for Corrosion and protection, 2025, 45(5): 1205-1218.

Download:

HTML

PDF(21511KB)
Export: BibTeX | EndNote (RIS)

Abstract

Herein, a method for predicting the service life-time of epoxy-based organic anti-abrasive coatings was stablished based on the integrative treatment of the acquired characteristics of microstructure images of multiple scales for organic coatings. Namely, the multiple scale microscopic structural images were collected by means of scanning electron microscopy, metallographic microscopy, laser confocal microscopy and other methods. Then the quantitative parameter data were extracted from the images using image recognition technology based on deep learning. A dynamic evolution relationship model of coating defect parameters with service time and a life prediction network model of organic coatings were constructed. The results indicate that the constructed evolutionary relationship curve model and network prediction model can accurately predict the lifespan of organic coatings.

Key words: deep learning image recognition organic coatings multi-scale feature fusion lifetime prediction

Received: 18 March 2025 32134.14.1005.4537.2025.092

ZTFLH:

TG174

Fund: National Natural Science Foundation of China(52271052);Liaoning Natural Science Foundation(2023-MSBA-043)

Corresponding Authors: MENG Fandi, E-mail: fandimeng@mail.neu.edu.cn;
QI Haixia, E-mail: qihaixia19861222@126.com

	Service

	E-mail this article
	Add to citation manager
	E-mail Alert
	RSS
	（）
	Articles by authors
	LI Jie
	MENG Fandi
	SUN Xuesi
	LI Jiani
	CHEN Sihan
	LI Zelan
	CHI Jianning
	QI Haixia
	WANG Fuhui
	LIU Li

URL:

https://www.jcscp.org/EN/10.11902/1005.4537.2025.092 OR https://www.jcscp.org/EN/Y2025/V45/I5/1205

Fig.1 Macroscopic topographies of the coating sample after immersion-wear cyclic test for 1 cyc (a), 5 cyc (b), 9 cyc (c), 11 cyc (d), 14 cyc (e), 16 cyc (f), 18 cyc (g) and 20 cyc (h)

Fig.2 SEM images of the coating sample after immersion-wear test for 1 cyc (a), 3 cyc (b), 5 cyc (c), 8 cyc (d), 11 cyc (e), 14 cyc (f), 16 cyc (g), 18 cyc (h), 20 cyc (i), and defect category of the coating (j)

Fig.3 Construction of convolutional neural networks for identifying suspicious crack regions.

Fig.4 Variations of the counts of wear mark defects with test cycle

Fig.5 Histograms of area-frequency distributions of resin shedding pits in the coating after immersion-wear cyclic test for different cycles: (a) 1 cyc, (b) 5 cyc, (c) 9 cyc, (d) 13 cyc, (e) 17 cyc, (f) 20 cyc

Fig.6 Metallurgical microscope images of the coating after immersion-wear cyclic test for 1 cyc (a), 2 cyc (b), 3 cyc (c), 4 cyc (d), 5 cyc (e) and 6 cyc (f)

Fig.7 Average contrasts and average dissimilarities of metallographic micrographs at 100 (a, b) and 200 (c, d) magnifications for the coating after immersion-wear cyclic test

Fig.8 2D (a1-e1) and 3D CLSM (a2-e2) images of the coating after immersion-wear test for 5 cyc (a), 9 cyc (b), 14 cyc (c), 17 cyc (d) and 20 cyc (e)

Fig.9 Variations of (a) Sq and (b) Sa determined by CLSM for the coating after immersion-wear cyclic test for different cycles

Fig.10 MMFCT network architecture diagram

Fig.11 Transformer network architecture diagram

Fig.12 Cross-modal feature fusion module

Fig.13 Comparison of loss curves for the four models: (a) SEM, (b) OM, (c) CLSM, (d) MMFCT

Table 1 Comparison of four predictive performance indicators between three single-scale deep learning models and the MMFCT integration model

Fig.14 Comparison of accuracy curves for the four models: (a) SEM, (b) OM, (c) CLSM, (d) MMFCT

Fig.15 Comparison of specificity curves for the four models: (a) SEM, (b) OM, (c) CLSM, (d) MMFCT

Fig.16 Comparison of sensitivity curves for the four models: (a) SEM, (b) OM, (c) CLSM, (d) MMFCT

Fig.17 Number of defects in the first 20 groups of shedding pits was the raw data after removing outliers

Fig.18 Proportion of defect area in the first 20 groups of shedding pits is the original data after removing outliers

Fig.19 Confusion matrix of data detection for the extended groups

Table 2 Classification accuracy results of life prediction model for the extended groups

[1]	Zimmermann N, Wang P H. A review of failure modes and fracture analysis of aircraft composite materials [J]. Eng. Fail. Anal., 2020, 115: 104692
[2]	Bakhshan H, Afrouzian A, Ahmadi H, et al. Progressive failure analysis of fiber-reinforced laminated composites containing a hole[J]. Int. J. Damage Mech., 2018, 27: 963
[3]	An D Y, Chen J Y, Liu Z, et al. A method of coating life prediction based on high temperature thermal shock life test and three-dimensional heat transfer analysis [J]. Sci. Rep., 2025, 15: 3029
[4]	Zhang C Z, Fu X Q. Applications and potentials of machine learning in optoelectronic materials research: An overview and perspectives [J]. Chin. Phys. B, 2023, 32: 126103
[5]	Chan C H, Sun M Z, Huang B L. Application of machine learning for advanced material prediction and design [J]. Ecomat, 2022, 4(4): 16
[6]	Xie W B, Zheng H, Li M Y, et al. Membership function-dependent local controller design for t-s fuzzy systems [J]. IEEE. Trans. Syst. Man. Cybern. Syst., 2022, 52(2): 814
[7]	Yoo Y, Baek J G. A novel image feature for the remaining useful lifetime prediction of bearings based on continuous wavelet transform and convolutional neural network [J]. Appl. Sci-Basel., 2018, 8: 1102
[8]	Du J W, Wu F, Yin J C, et al. Polyline simplification based on the artificial neural network with constraints of generalization knowledge [J]. Cartogr. Geogr. Inf. Sci., 2022, 49(4): 313
[9]	Ma H, Liu R, Cui Y, et al. Effect of hydrostatic pressure on corrosion behavior of Cr/GLC laminated coatings [J]. J. Chin. Corros. Prot., 2025, 45: 103
	马宏宇, 刘叡, 崔宇等. 静水压力对Cr/GLC叠层涂层腐蚀行为的影响 [J]. 中国腐蚀与防护学报， 2025, 45: 103 doi: 10.11902/1005.4537.2024.153
[10]	Liu H, Zhang Y F. Image-driven structural steel damage condition assessment method using deep learning algorithm [J]. Measurement, 2019, 133: 168
[11]	Zhang X, Yao J Y, Wu Y L, et al. A method for predicting the creep rupture life of small-sample materials based on parametric models and machine learning models [J]. Materials, 2023, 16: 6804
[12]	Chen Y F, Meng F D, Liu L, et al. Life prediction of organic coatings based on grey system theory in coupled deep-sea pressure-flow velocity environment [J]. Equip. Environ. Eng., 2023, 20(2):64
	陈宇凡, 孟凡帝, 刘莉等. 深海压力-流速耦合环境下基于灰色系统理论的有机涂层寿命预测研究 [J]. 装备环境工程, 2023, 20(2): 64
[13]	Geng G Q, Lin J, Liu L J, et al. Prediction method for life of anti-corrosion coating for steel bridge [J]. Natur. Sci. Ed., 2006, (5): 43
	耿刚强, 林杰, 刘来君等. 钢桥防腐蚀涂层寿命的预测方法 [J]. 长安大学学报(自然科学版), 2006, (5): 43
[14]	Choudhary K, Decost B, Chen C, et al. Recent advances and applications of deep learning methods in materials science [J]. NPJ Comput. Mater., 2022, 8(1): 548
[15]	Zarkevich N A. Theoretical and computational methods for accelerated materials discovery [J]. Mod. Phys. Lett. B, 2021, 35(12): 213
[16]	Zhai H, Zhai Y J. Optimization design of ferry material performance test system based on artificial intelligence [J]. J. Nanomater., 2022, 2022: 1155
[17]	Yu W J, Wang T C, Zhao D Y, et al. Research on life prediction model of closed corrosion-resistant coatings [J]. J.Chin.Soc.Corros.Prot., 2024, 44: 1617
	禹文娟, 王天丛, 赵东杨等. 封闭型耐蚀涂层的寿命预测模型研究 [J]. 中国腐蚀与防护学报， 2024, 44: 1617 doi: 10.11902/1005.4537.2023.383
[18]	Lopes C F, Machado G S D, Mendes P S N, et al. Analysis of copper and zinc alloy surface by exposure to alcohol aqueous solutions and sugarcane liquor [J]. J. Mater. Res. Technol., 2020, 9(2): 2545
[19]	Da Silva P C, Junior C F L, Huguenin J a O, et al. Investigation of copper and zinc alloy surface exposed to corrosion environment by digital image processing [J]. J. Mater. Res. Technol., 2023, 24: 9743
[20]	Yao Y, Yang Y, Wang Y P, et al. Artificial intelligence-based hull structural plate corrosion damage detection and recognition using convolutional neural network [J]. Appl. Oce. Res., 2019, 90: 101823
[21]	Pan J, Liu F, Feng J, et al. Accurate recognition of micromorphology images of epoxy coatings for deep-sea environments based on a deep learning super-resolution method [J]. Corros. Commun., 2025, 19: 14

[1]	SHEN Zhiyuan, SHAN Guangbin, CHEN Mindong, LIU Yuanshuang. An Electrochemical Noise Signal Recognition Method for Corrosion Monitoring[J]. 中国腐蚀与防护学报, 2025, 45(5): 1433-1440.
[2]	YU Wenjuan, WANG Tiancong, ZHAO Dongyang, XIANG Xueyun, WU Hang, WANG Wen. Lifetime Prediction Model for Barrier-type Corrosion-resistant Coating[J]. 中国腐蚀与防护学报, 2024, 44(6): 1617-1624.
[3]	WANG Kai, LI Chenpei, LU Jinling, WANG Zhenjiang, WANG Wei. Cavitation Resistance of NiCoCrFeNb_0.45 Eutectic High Entropy Alloy for Hydraulic Machinery[J]. 中国腐蚀与防护学报, 2023, 43(5): 1079-1086.
[4]	WANG Minghao, WANG Huan, LIU Rui, MENG Fandi, LIU Li, WANG Fuhui. Research on Image Recognition for NiCrAlY Coating/N5 High-temperature Alloy System Based on Deep Learning Method[J]. 中国腐蚀与防护学报, 2022, 42(4): 583-589.
[5]	Xiaodong LIN,Qunjia PENG,En-Hou HAN,Wei KE. Review of Thermal Aging of Nuclear Grade Stainless Steels[J]. 中国腐蚀与防护学报, 2017, 37(2): 81-92.
[6]	ZHANG Wei, WANG Jia, ZHAO Zengyuan, LIU Xueqing. EIS STUDY ON THE DETERIORATION PROCESS OF ORGANIC COATINGS UNDER IMMERSION AND CYCLIC WET-DRY CONDITIONS[J]. 中国腐蚀与防护学报, 2011, 31(5): 329-335.
[7]	Jiming Hu; Jianqing Zhang; Deming Xie. WATER TRANSPORT IN ORGANIC COATINGS(Ⅱ) A complicated actual trend[J]. 中国腐蚀与防护学报, 2002, 22(6): 371-374 .
[8]	Jiming Hu; Jianqing Zhang; Deming Xie. WATER TRANSPORT IN ORGANIC COATINGSⅠ: FICKIAN DIFFUSION[J]. 中国腐蚀与防护学报, 2002, 22(5): 311-315 .

No Suggested Reading articles found!

Viewed

Full text

Abstract

Cited

Shared

Discussed